code: def __init__(
      self,
      in_channels,
      char_num,
      max_text_length,
      num_heads,
      num_encoder_tus,
      num_decoder_tus,
      hidden_dims,
  ):
      super(GSRM, self).__init__()
      self.char_num = char_num
      self.max_length = max_text_length
      self.num_heads = num_heads
      self.num_encoder_TUs = num_encoder_tus
      self.num_decoder_TUs = num_decoder_tus
      self.hidden_dims = hidden_dims
  
      self.fc0 = paddle.nn.Linear(
          in_features=in_channels, out_features=self.char_num
      )
      self.wrap_encoder0 = WrapEncoder(
          src_vocab_size=self.char_num + 1,
          max_length=self.max_length,
          n_layer=self.num_decoder_TUs,
          n_head=self.num_heads,
          d_key=int(self.hidden_dims / self.num_heads),
          d_value=int(self.hidden_dims / self.num_heads),
          d_model=self.hidden_dims,
          d_inner_hid=self.hidden_dims,
          prepostprocess_dropout=0.1,
          attention_dropout=0.1,
          relu_dropout=0.1,
          preprocess_cmd="n",
          postprocess_cmd="da",
          weight_sharing=True,
      )
  
      self.wrap_encoder1 = WrapEncoder(
          src_vocab_size=self.char_num + 1,
          max_length=self.max_length,
          n_layer=self.num_decoder_TUs,
          n_head=self.num_heads,
          d_key=int(self.hidden_dims / self.num_heads),
          d_value=int(self.hidden_dims / self.num_heads),
          d_model=self.hidden_dims,
          d_inner_hid=self.hidden_dims,
          prepostprocess_dropout=0.1,
          attention_dropout=0.1,
          relu_dropout=0.1,
          preprocess_cmd="n",
          postprocess_cmd="da",
          weight_sharing=True,
      )
  
      self.mul = lambda x: paddle.matmul(
          x=x,
          y=self.wrap_encoder0.prepare_decoder.emb0.weight,
          transpose_y=True,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn, ParamAttr
  from paddle.nn import functional
  import numpy
  from .self_attention import WrapEncoderForFeature
  from .self_attention import WrapEncoder
  from paddle.static import Program
  from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
  from collections import OrderedDict
label_desc: Use image node_id 1 to create a new GSRM object from inherited base classes: nn with example: obj = GSRM(in_channels, char_num, max_text_length, num_heads, num_encoder_tus, num_decoder_tus, hidden_dims)